Objective To analyze the relationship of protein intake level with high-sensitivity C-reactive protein (hs-CRP), interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α) in patients with diabetic kidney disease (DKD) and its influence on prognosis. Methods A total of 325 patients with DKD admitted from June 2021 to June 2024 were included and classified into a low protein group and a high protein group. The levels of hs-CRP, IL-6, and TNF-α were compared between the two groups of patients before follow-up and after 1 year of follow-up. The correlation was analyzed by linear mixed model. The incidence of endpoint events during follow-up and disease progression-free survival time were compared between the two groups. Results The inflammatory indicators exhibited no statistical differences between the two groups before follow-up (P>0.05). After 1 year of follow-up, the levels of hs-CRP, IL-6, and TNF-α in the two groups were higher than those before follow-up, and the levels in the high protein group were higher than those in the low protein group (P<0.05). Linear mixed model analysis suggested that the protein intake level, time, and the interaction term of protein intake level × time were correlated with the changes of hs-CRP, IL-6 and TNF-α levels (P<0.05). The incidence rate of endpoint events in the high-protein group during follow-up was 24.84% (40/161), which was significantly higher than 12.80% (21/164) in the low-protein group (χ²=7.724, P=0.005). The disease progression-free survival time was longer in the low-protein group than that in the high-protein group (Log Rank χ²=9.007, P=0.003). Conclusion The level of protein intake in patients with diabetic kidney disease is closely related to inflammatory response and prognosis.

Non-invasive body fluids contain a wealth of health-related biomarkers.Monitoring these biomarkers can provide valuable information for disease diagnosis,health management,drug abuse screening,and sports performance optimization.In this work,a carbon nanotube-polysiloxane(CNT-Putty)-based wearable electrochemical sensor was constructed on glove by screen-printing method.This electrode material had not only a simple composition,but also a relatively simple synthesis process.In addition,the electrode exhibited superior electrochemical performance compared to commercial screen-printed electrodes.When applied to uric acid(UA)detection in three different body fluids,the CNT-Putty working electrode demonstrated excellent linearity,selectivity,and a low detection limit.The wearable glove sensor could successfully monitor UA levels in body fluids under varying dietary conditions,indicating its potential for personalized UA monitoring and management.

Objective To explore a deep learning-based automatic bone age estimation model for elbow joint X-ray images of Chinese Han adolescents and children and evaluate its performance.Methods A total of 943(517 males and 426 females)elbow joint frontal view X-ray images of Chinese Han ado-lescents and children aged 6.00 to<16.00 years were collected from East,South,Central and North-west China.Three experimental schemes were adopted for bone age estimation.Scheme 1:Directly in-put preprocessed images into the regression model;Scheme 2:Train a segmentation network using"key elbow joint bone annotations"as labels,then input segmented images into the regression model;Scheme 3:Train a segmentation network using"full elbow joint bone annotations"as labels,then in-put segmented images into the regression model.For segmentation,the optimal model was selected from U-Net,UNet++and TransUNet.For regression,VGG16,VGG19,InceptionV2,InceptionV3,ResNet34,ResNet50,ResNet101 and DenseNet121 models were selected for bone age estimation.The dataset was randomly split into 80%(754 samples)for training and validation for model fitting and hyperparameter tuning,and 20%(189 samples)as an internal test set to test the performance of the trained model.An additional 104 elbow joint X-ray images from the same demographic and age group were col-lected and used as an external test set.Model performance was evaluated by comparing the mean ab-solute error(MAE),root mean square error(RMSE),accuracies within±0.7 years(P±0.7 years)and±1.0 years(P±1.0 years)between the estimated age and the actual age,and by drawing radar charts,scat-ter plots,and heatmaps.Results When segmented with Scheme 3,the UNet++model achieved good segmentation performance with a segmentation loss of 0.000 4 and an accuracy of 93.8%at a learning rate of 0.000 1.In the internal test set,the DenseNet121 model with Scheme 3 yielded the best results with MAE,P±0.7 years and P±1.0 years being 0.83 years,70.03%,and 84.30%,respectively.In the external test set,the DenseNet121 model with Scheme 3 also performed best,with an average MAE of 0.89 years and an average RMSE of 1.00 years.Conclusion When performing automatic bone age estima-tion using elbow joint X-ray images in Chinese Han adolescents and children,it is recommended to use the UNet++model for segmentation.The DenseNet121 model with Scheme 3 achieves optimal per-formance.Using segmentation networks,especially that trained with annotation areas encompassing the full elbow joint including the distal humerus,proximal radius,and proximal ulna,can improve the ac-curacy of bone age estimation based on elbow joint X-ray images.

Objective To explore a deep learning network model suitable for bone age estimation using shoulder joint X-ray images in Chinese Han adolescents.Methods A retrospective collection of 1 286 shoulder joint X-ray images of Chinese Han adolescents aged 12.0 to<18.0 years(708 males and 578 females)was conducted.Using random sampling,approximately 80%of the samples(1 032 cases)were selected as the training and validation sets for model learning,selection and optimization,and the other 20%samples(254 cases)were used as the test set to evaluate the model's generalization ability.The original single-channel shoulder joint X-ray images and dual-channel inputs combining original images with segmentation labels(manually annotated shoulder joint regions multiplied pixel-by-pixel with original images,followed by segmentation via the U-Net++network to retain only key shoulder joint region information)were respectively input into four network models,namely VGG16,ResNet18,ResNet50 and DenseNet121 for bone age estimation.Additionally,manual bone age estimation was con-ducted on the test set data,and the results were compared with the four network models.The mean absolute error(MAE),root mean square error(RMSE),coefficient of determination(R2),and Pear-son correlation coefficient(PCC)were used as main evaluation indicators.Results In the test set,the bone age estimation results of the four models with dual-channel input of shoulder joint X-ray images outperformed those with single-channel input in all four evaluation indicators.Among them,DenseNet121 with dual-channel input achieved best results with MAE of 0.54 years,RMSE of 0.82 years,R2 of 0.76,and PCC(r)of 0.88.Manual estimation yielded an MAE of 0.82 years,ranking second only to dual-channel DenseNet121.Conclusion The DenseNet121 model with dual-channel input combined with original images and segmentation labels is superior to manual evaluation results,and can effectively estimate the bone age of Chinese Han adolescents.

The present study delves into the cellular mechanisms underlying the antiviral effects of dehydrodiisoeugenol(DEH) by focusing on the transcription factor EB(TFEB)/autophagy-lysosome pathway. The cell counting kit-8(CCK-8) was utilized to assess the impact of DEH on the viability of human non-small cell lung cancer cells(A549). The inhibitory effect of DEH on the replication of influenza A virus(H1N1) was determined by real-time quantitative polymerase chain reaction(RT-qPCR). Western blot was employed to evaluate the influence of DEH on the expression level of the H1N1 virus nucleoprotein(NP). The effect of DEH on the fluorescence intensity of NP was examined by the immunofluorescence assay. A mouse model of H1N1 virus infection was established via nasal inhalation to evaluate the therapeutic efficacy of 30 mg·kg~(-1) DEH on H1N1 virus infection. RNA sequencing(RNA-seq) was performed for the transcriptional profiling of mouse embryonic fibroblasts(MEFs) in response to DEH. The fluorescent protein-tagged microtubule-associated protein 1 light chain 3(LC3) was used to assess the autophagy induced by DEH. Western blot was employed to determine the effect of DEH on the autophagy flux of LC3Ⅱ/LC3Ⅰ under viral infection conditions. Lastly, the role of TFEB expression in the inhibition of DEH against H1N1 infection was evaluated in immortalized bone marrow-derived macrophage(iBMDM), both wild-type and TFEB knockout. The results revealed that the half-maximal inhibitory concentration(IC_(50)) of DEH for A549 cells was(87.17±0.247)μmol·L~(-1), and DEH inhibited H1N1 virus replication in a dose-dependent manner in vitro. Compared with the H1N1 virus-infected mouse model, the treatment with DEH significantly improved the body weights and survival time of mice. DEH induced LC3 aggregation, and the absence of TFEB expression in iBMDM markedly limited the ability of DEH to counteract H1N1 virus replication. In conclusion, DEH exerts its inhibitory activity against H1N1 infection by activating the TFEB/autophagy-lysosome pathway.
Influenza A Virus, H1N1 Subtype/genetics* ; Animals ; Autophagy/drug effects* ; Humans ; Mice ; Basic Helix-Loop-Helix Leucine Zipper Transcription Factors/genetics* ; Influenza, Human/metabolism* ; Lysosomes/metabolism* ; Orthomyxoviridae Infections/genetics* ; Eugenol/pharmacology* ; Antiviral Agents/pharmacology* ; Virus Replication/drug effects* ; A549 Cells ; Male

This study employed bioinformatics to screen the feature genes related to efferocytosis in diabetic kidney disease(DKD) and explores traditional Chinese medicine(TCM) regulating these feature genes. The GSE96804 and GSE30528 datasets were integrated as the training set, and the intersection of differentially expressed genes and efferocytosis-related genes(ERGs) was identified as DKD-ERGs. Subsequently, correlation analysis, protein-protein interaction(PPI) network construction, enrichment analysis, and immune infiltration analysis were performed. Consensus clustering was conducted on DKD patients based on the expression levels of DKD-ERGs, and the expression levels, immune infiltration characteristics, and gene set variations between different subtypes were explored. Eight machine learning models were constructed and their prediction performance was evaluated. The best-performing model was evaluated by nomograms, calibration curves, and external datasets, followed by the identification of efferocytosis-related feature genes associated with DKD. Finally, potential TCMs that can regulate these feature genes were predicted. The results showed that the training set contained 640 differentially expressed genes, and after intersecting with ERGs, 12 DKD-ERGs were obtained, which demonstrated mutual regulation and immune modulation effects. Consensus clustering divided DKD into two subtypes, C1 and C2. The support vector machine(SVM) model had the best performance, predicting that growth arrest-specific protein 6(GAS6), S100 calcium-binding protein A9(S100A9), C-X3-C motif chemokine ligand 1(CX3CL1), 5'-nucleotidase(NT5E), and interleukin 33(IL33) were the feature genes of DKD. Potential TCMs with therapeutic effects included Astragali Radix, Trionycis Carapax, Sargassum, Rhei Radix et Rhizoma, Curcumae Radix, and Alismatis Rhizoma, which mainly function to clear heat, replenish deficiency, activate blood, resolve stasis, and promote urination and drain dampness. Molecular docking revealed that the key components of these TCMs, including β-sitosterol, quercetin, and sitosterol, exhibited good binding activity with the five target genes. These results indicated that efferocytosis played a crucial role in the development and progression of DKD. The feature genes closely related to both DKD and efferocytosis, such as GAS6, S100A9, CX3CL1, NT5E, and IL33, were identified. TCMs such as Astragali Radix, Trionycis Carapa, Sargassum, Rhei Radix et Rhizoma, Curcumae Radix, and Alismatis Rhizoma may provide a new therapeutic strategy for DKD by regulating efferocytosis.
Humans ; Computational Biology ; Diabetic Nephropathies/physiopathology* ; Protein Interaction Maps ; Medicine, Chinese Traditional ; Drugs, Chinese Herbal ; Phagocytosis/genetics* ; Efferocytosis

With the rise of data-intensive research, data literacy has become a critical capability for improving scientific data quality and achieving artificial intelligence (AI) readiness. In the biomedical domain, data are characterized by high complexity and privacy sensitivity, calling for robust and systematic data management skills. This paper reviews current trends in scientific data governance and the evolving policy landscape, highlighting persistent challenges such as inconsistent standards, semantic misalignment, and limited awareness of compliance. These issues are largely rooted in the lack of structured training and practical support for researchers. In response, this study builds on existing data literacy frameworks and integrates the specific demands of biomedical research to propose a comprehensive, lifecycle-oriented data literacy competency model with an emphasis on ethics and regulatory awareness. Furthermore, it outlines a tiered training strategy tailored to different research stages-undergraduate, graduate, and professional, offering theoretical foundations and practical pathways for universities and research institutions to advance data literacy education.
Artificial Intelligence ; Humans ; Biomedical Research

Objective To study a method for determining the cumulative dose of low-energy neutrons ( < 100 keV) independently based on a CR-39 detector. Methods According to the theory of track etching kinetics, the differences in the tracks formed by low-energy neutrons or fast neutrons in a BN + CR-39 detector under broad-spectrum neutron irradiation were analyzed. A method was proposed to identify the tracks produced by low-energy neutrons under specific etching conditions while avoiding interference from fast neutron tracks. Results Experimental results demonstrated that the BN + CR-39 detector using TASTRAK PADC CR-39 track-detecting plastic could independently detect the tracks of low-energy neutrons when etched in a 6.25 mol/L NaOH solution for 1 h. The track density showed a good linear relationship with the ambient dose equivalent of low-energy neutrons, and the calibration coefficient was 0.0366 μSv·tr⁻¹·cm². Conclusion The BN + CR-39 detector proposed in this study demonstrated high sensitivity to low-energy neutrons. The track identification method can be used for effective and independent determination of the cumulative dose equivalent of low-energy neutrons.

Intentional tooth replantation (ITR) is an advanced treatment modality and the procedure of last resort for preserving teeth with inaccessible endodontic or resorptive lesions. ITR is defined as the deliberate extraction of a tooth; evaluation of the root surface, endodontic manipulation, and repair; and placement of the tooth back into its original socket. Case reports, case series, cohort studies, and randomized controlled trials have demonstrated the efficacy of ITR in the retention of natural teeth that are untreatable or difficult to manage with root canal treatment or endodontic microsurgery. However, variations in clinical protocols for ITR exist due to the empirical nature of the original protocols and rapid advancements in the field of oral biology and dental materials. This heterogeneity in protocols may cause confusion among dental practitioners; therefore, guidelines and considerations for ITR should be explicated. This expert consensus discusses the biological foundation of ITR, the available clinical protocols and current status of ITR in treating teeth with refractory apical periodontitis or anatomical aberration, and the main complications of this treatment, aiming to refine the clinical management of ITR in accordance with the progress of basic research and clinical studies; the findings suggest that ITR may become a more consistent evidence-based option in dental treatment.
Humans ; Tooth Replantation/methods* ; Consensus ; Periapical Periodontitis/surgery*

Digital technologies have become an integral part of complete denture restoration. With advancement in computer-aided design and computer-aided manufacturing (CAD/CAM), tools such as intraoral scanning, facial scanning, 3D printing, and numerical control machining are reshaping the workflow of complete denture restoration. Unlike conventional methods that rely heavily on clinical experience and manual techniques, digital technologies offer greater precision, predictability, and efficacy. They also streamline the process by reducing the number of patient visits and improving overall comfort. Despite these improvements, the clinical application of digital complete denture restoration still faces challenges that require further standardization. The major issues include appropriate case selection, establishing consistent digital workflows, and evaluating long-term outcomes. To address these challenges and provide clinical guidance for practitioners, this expert consensus outlines the principles, advantages, and limitations of digital complete denture technology. The aim of this review was to offer practical recommendations on indications, clinical procedures and precautions, evaluation metrics, and outcome assessment to support digital restoration of complete denture in clinical practice.
Humans ; Denture, Complete ; Computer-Aided Design ; Denture Design/methods* ; Consensus ; Printing, Three-Dimensional